Convergence Accelerator Track J Phase 2: Rapid Detection Technologies and Decision-Support Systems for Safe Food Systems

University Of Missouri-Columbia, Columbia MO

Investigators

Abstract

Salmonella is a leading cause of foodborne illness, resulting in 1.35 million infections, 26,500 hospitalizations, 420 deaths, and costs the U.S. economy $4.1 billion annually. Despite nationwide efforts, the infection rates have been unchanged for three decades, making it a "One Health" issue. To cope with this challenge, this project establishes wide-ranging partnerships with the poultry industry, end-to-end supply chains, food banks, and Extension educators. The objective is to create a transformative sensor-enabled decision support system (DSS; termed as SENS-D), which incorporates multiple rapid sensing technologies and sensing systems prototypes, along with visualization, prediction, and optimization capabilities to detect and mitigate Salmonella contamination throughout the poultry supply chain. SENS-D is envisioned to provide data-driven solutions that significantly improve food safety, efficiency, and resilience. The sensing systems are portable, easy-to-use, accurate, and cost-effective. By integrating sensor results with the DSS, this technology will ensure a secure food supply. SENS-D can be adapted to detect various pathogens in other food products including beef, pork, dairy, and produce, ultimately reducing the $152 billion economic burden of foodborne illness in the U.S. To maximize impact, the project will engage various experts and end-users. Additionally, this initiative will train the American workforce to tackle food safety by creating new training opportunities. This project develops three innovative sensing technologies and user-centric prototypes for portable systems, transforming poultry testing by enabling rapid, multiplex, and quantitative detection and surveillance of Salmonella within 10-60 minutes. The Surface Enhanced Raman Spectroscopy sensor integrates metal nanoantennas on a side-polished multimode optical fiber core, enabling rapid, quantitative detection of Salmonella serovars. The impedance-based biosensor concentrates Salmonella to a detectable threshold, capturing and identifying the pathogen, while the nanopore-facilitated, multi-locus checkpoint sequencing sensor differentiates Salmonella through single-nucleotide variations. The DSS employs advanced analytics and artificial intelligence (AI) to monitor, predict, and mitigate Salmonella risks in a sensor-enabled poultry supply chain. It utilizes a cloud based One Health data environment for real-time data integration from the sensing system. Advanced statistical and machine learning techniques will predict Salmonella levels and product shelf-life. Optimization will be used for sensor placement, intelligent distribution of food, and workforce planning to facilitate implementation of sensors, while achieving multiple performance metrics of safety, efficiency, and resilience. An analytical toolkit will determine the efficacy of Salmonella mitigation. Through stakeholder and end-user engagement, SENS-D has the potential to transform Salmonella mitigation and significantly enhance food safety. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.